Certification and Credibility: Do Seed Certification Systems in Uganda Help Signal Quality to Farmers?
Bibliographic record
Abstract
ABSTRACT The value of purchased seed is a product of its underlying genetic information. However, farmers purchasing seed face a classic information asymmetry problem, as varietal identity is not directly observable. In this article we investigate the effectiveness of two certification systems in addressing this issue in the context of Uganda: the formal certification system (predominantly private sector) and the FAO's quality declared system (certification for seed produced mainly by farmers' groups). Using a large, nationally representative panel dataset spanning thirteen growing seasons, and controlling for time‐invariant household and plot‐level unobservables, we find that cultivating purchased quality declared improved seed results in measurable yield increases relative to saved seed. Surprisingly, however, certified improved seed is shown to provide no yield benefits over seed saved from previous seasons, despite its higher cost. Our findings suggest that input heterogeneity and information asymmetry in seed markets may be key constraints to the successful diffusion of improved maize varieties in Uganda, and that the formal seed certification system may not have provided an adequate signal of seed quality to farmers during the time period covered by the panel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".